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    <title>Self-Distillation Dual-Memory Online Hashing with Hash Centers for Streaming Data Retrieval</title>
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            <h1>Self-Distillation Dual-Memory Online Hashing with Hash Centers for Streaming Data Retrieval</h1>
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                <div class="col-5 text-center">
                    <a href="https://zcyueternal.github.io/"><img src="../imgs/miaomiao.png"><p>Chong-Yu Zhang<sup>1</sup></p></a>
                </div>
                <div class="col-5 text-center">
                    <a href="https://faculty.sdu.edu.cn/luoxin/zh_CN/index.htm"><img src="../imgs/xinluo.png"><p>Xin Luo<sup>1</sup></p></a>
                </div>
                <div class="col-5 text-center">
                    <a href="https://mima.sdu.edu.cn/Members/zhanyuwei/index.html"><img src="../imgs/yuweizhan.png"><p>Yu-Wei Zhan<sup>1</sup></p></a>
                </div>
                <div class="col-5 text-center">
                    <a href="https://researchers.uq.edu.au/researcher/40449"><img src="../imgs/pengfeizhang.png"><p>Peng-Fei Zhang<sup>2</sup></p></a>
                </div>
                <div class="col-5 text-center">
                    <a href="https://www.sc.sdu.edu.cn/info/1044/3963.htm"><img src="../imgs/zhenduochen.png"><p>Zhen-Duo Chen<sup>1</sup></p></a>
                </div>
                <div class="col-3 text-center">
                    <a href="https://yxinwang.github.io/"><img src="../imgs/yongxinwang.jpg"><p>Yongxin Wang<sup>3</sup></p></a>
                </div>
                <div class="col-3 text-center">
                    <a href="https://faculty.ustc.edu.cn/yangxun/"><img src="../imgs/xunyang.png"><p>Xun Yang<sup>4</sup></p></a>
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                <div class="col-3 text-center">
                    <a href="https://faculty.sdu.edu.cn/xuxinshun/zh_CN/index.htm"><img src="../imgs/xinshunxu.png"><p>Xin-Shun Xu<sup>1</sup></p></a>
                </div>

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            <div class="affil-row">
                <div class="col-2 text-center"><a href="https://www.ed.ac.uk/informatics"><p><sup>1</sup>Shandong University</p></a></div>
                <div class="col-2 text-center"><a href="https://www.saicmotor.com/english/index.shtml"><p><sup>2</sup>University of Queensland</p></a></div>
                <div class="col-2 text-center"><a href="https://www.ed.ac.uk/informatics"><p><sup>3</sup>Shandong Jianzhu University</p></a></div>
                <div class="col-2 text-center"><a href="https://www.saicmotor.com/english/index.shtml"><p><sup>4</sup>University of Science and Technology</p></a></div>
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        <div class="parent">
              <a href="https://dl.acm.org/doi/10.1145/3581783.3612119"><button class="bs"><span class="fa fa-file-pdf-o fa-fw"></span> Paper</button></a>
              <a href="https://github.com/ZCyueternal/SDOH-HC"><button class="bs"><span class="fa fa-github fa-fw"></span> Code</button></a>
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            <p class="caption">
                The overall framework of SDOH-HC. The pink region illustrates hash codes learning step while the beige region represents hash function learning step. Within hash codes learning, mistyrose and lightcyan areas are hash centers self-distillation and exemplar memory, respectively.
            </p>
        </section>

        <h2>Abstract</h2>
        <hr>
        <div class="flex-row">
                <div style="width: 55%">
                    <section id="abstract">
                        With the continuous generation of massive amounts of multimedia data nowadays, hashing has demonstrated significant potentials for large-scale search. To handle the emerging needs for streaming data retrieval, online hashing is drawing more and more attention. For online scenario, data distribution may change and concept drifts may occur as new data is continuously added to the database. Inevitably, hashing models may lose or disrupt the previously obtained knowledge when learning from new information, which is called the problem of catastrophic forgetting. In this paper, we propose a new online hashing method called Self-distillation Dualmemory Online Hashing with Hash Centers, which is abbreviated to SDOH-HC, to overcome this challenge. Specifically, SDOH-HC contains replay and distillation modules. For replay, a dual-memory mechanism is proposed which involves hash centers and exemplars. For knowledge distillation, we let hash centers distill information from themselves, i.e., the version of last round. Additionally, a new objective function is further built on above modules and is solved discretely to learn hash codes. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our method.
                    </section>
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        <!-- <h2>Qualitative results</h2>
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                sss
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        <h3>Motion Segmentation</h3>
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            <figure style= margin-bottom: 0"></figure>
            <img class="centered" width="80%" src="motion_seg.png">
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                    sss
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    <div class="container">
        <br>
        <h2>Citation</h2>
            <hr>
        <section id="bibtex">
            <pre>
                <code>@inProceedings{zhang2023self,
                author = {Zhang, Chong-Yu and Luo, Xin and Zhan, Yu-Wei and Zhang, Peng-Fei and 
                    Chen, Zhen-Duo and Wang Yongxin and Yang, Xun and Xu, Xin-Shun},
                title = {Self-distillation dual-memory online hashing with hash centers for streaming data retrieval},
                booktitle = {Proceedings of the ACM International Conference on Multimedia},
                year = {2023}
                }
                </code>
            </pre>
        </section>

        <br>
        <h2>Acknowledgments</h2>
        <hr>
        <section id="acknowledgements">
            This work was supported in part by the National Natural Science Foundation of China under Grant 62202278, 62172256, 62202272, U22A2094, in part by Natural Science Foundation of Shandong Province under Grant ZR2020QF036, ZR2019ZD06, ZR2022QF116, and in part by the Young Scholars Program of Shandong University
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